AI Activity Guidelines
Using AI to Learn Data Science
This assignment rewards thinking, judgment, and learning, NOT perfect answers. Strong work shows curiosity, skepticism, and clear reasoning. If you have questions about appropriate AI use, ask before submitting.
- Work in groups of 3 (or 4).
- Roles: Prompt Engineer, Data Science Auditor, Synthesizer. See Section 4.
- Group submits: (1) AI Interaction Log, (2) Human Authored Synthesis, (3) Slides. See Section 6.
- Each student submits: Individual Reflection (150 to 200 words) See Section 6.
- Email Dr. Yu all materials by 11:59 PM on the day your team presents. See Section 7.
- Not allowed: AI text copied into synthesis; undocumented AI use.
- Grading: rubric categories and total points. See Section 8
In this activity, you will use an AI as a learning partner to explore a data science concept from this course. The goal is NOT to report what AI says. The goal is to learn to:
Ask effective questions
Evaluate the quality and limitations of AI responses
Apply data and modeling reasoning
Synthesize understanding in your own words
This activity mirrors how data scientists increasingly use AI in professional practice: as a tool that requires human judgment, verification, and interpretation.
Learning Objectives
By completing this activity, you will be able to:
Use AI strategically to support learning in data science
Critically evaluate AI-generated explanations and claims
Demonstrate conceptual understanding of a data science topic
Communicate statistical reasoning clearly and accurately
Reflect on what human expertise adds beyond AI output
Task Overview
Your group will:
Receive a data science question or concept from the course
Use an AI to help you learn about this topic
Critically evaluate the AI’s responses
Produce a corrected, human-authored explanation
Present your findings to the class
Group Structure
- Groups of 3 (or 4) students
- Each group completes one shared investigation
- Each student has a defined role and submits an individual reflection
Assigned Roles (Rotate Within the Group)
Each group must assign the following roles:
- Prompt Engineer
Primary responsibility: Design purposeful prompts with a clear learning goal and document the AI interaction log.
- Data Science Auditor
Primary responsibility: Checks AI responses for correctness, assumptions, and limitations.
- Synthesizer
Primary responsibility: Convert the group work into a clear human authored explanation and leads the presentation.
[Note:] One team has four members, so two students may share one role. If a role is shared, each student must complete a distinct subtask and clearly label their contributions in their individual reflection. Specific subtask assignments will be provided with your team’s topic.
Approved AI Use
You are expected to use AI for this assignment.
You must:
- Design your own prompts
- Document AI interactions
- Critically evaluate AI responses
You may NOT:
- Submit AI text verbatim as your own explanation
- Rely on AI without critique or verification
- Hide or omit AI usage
Transparency is required.
Required Deliverables
- AI Interaction Log (Group Submission)
Submit a short log documenting your AI use.
Include:
- 3–5 prompts used
- Selected AI responses
- A brief annotation after each response addressing:
- What was the goal of this prompt?
- What did AI get right?
- What was incomplete, misleading, or incorrect?
This log is evidence of your learning process.
- Human-Authored Synthesis (Group Submission)
Write a clear explanation of the concept in your own words.
Your synthesis must:
- Be conceptually accurate
- Improve upon or correct AI output
- Explain assumptions, limitations, or implications
- Be understandable to a classmate
AI-generated text should not appear verbatim.
- Group Presentation (12–15 minutes)
Your presentation must follow this structure:
- The question or concept you investigated
- What AI suggested
- Where AI fell short or made errors
- Your corrected understanding
- One key takeaway for future data science work
Slides should be clear, concise, and focused on reasoning rather than volume.
- Individual Reflection (Individual Submission)
Each student submits a 150–200 word reflection addressing:
- What did you personally learn from this activity?
- What did AI help you understand?
- What did you need to add, correct, or rethink yourself?
- How did your role contribute to the group’s work?
This reflection is used to assess individual understanding and contribution.
Submission
- Submit your AI Interaction Log, Human-Authored Synthesis, and Individual Reflection as a single PDF, or provide a direct link to downloadable PDFs. Indicate who plays which role.
- Submit your slides in your preferred format, or provide a direct link to view the slides.
- Email all materials to cheng-han.yu@marquette.edu.
- Deadline: 11:59 PM on the day your team presents.
- No late submission of any work is allowed.
Evaluation Criteria
Your group work will be evaluated based on:
- Quality and intentionality of AI prompts
- Critical evaluation of AI responses
- Depth and accuracy of data science understanding
- Quality of human synthesis beyond AI output
- Clarity and organization of communication
Individual reflections will be used to adjust individual scores if contributions differ.
Rubric Summary
| Criterion | Points |
|---|---|
| AI Prompt Quality | 20 |
| AI Documentation | 15 |
| Critical Evaluation of AI | 25 |
| Data Science Understanding & Synthesis | 25 |
| Presentation and Q&A | 10 |
| Participation* | 5 |
| Total | 100 |
In-Class Participation
The presenting group must prepare one or more questions for the other five groups.
Each non-presenting group must ask the presenting group at least one question.
Each student must submit a brief note describing what they learned from the presentation.
Grading Rubric
Total: 100 points
This rubric is used for all groups, regardless of topic. Grades reflect how you used AI to learn data science, not what AI produced.
AI Prompt Quality and Intentionality (20 points)
Evaluates: Whether AI was used strategically to support learning.
| Level | Description | Points |
|---|---|---|
| Excellent | Prompts are clear, targeted, and iterative. Each prompt has a specific learning goal, and follow-up prompts show refinement based on prior AI responses. | 18–20 |
| Competent | Prompts are reasonable and relevant but mostly one-shot or broad. Limited evidence of iteration or strategic refinement. | 14–17 |
| Developing | Prompts are vague, generic, or copied. Little connection between prompts and learning goals. | 8–13 |
| Insufficient | Prompts are missing, inappropriate, or unrelated to the assigned topic. | 0–7 |
Documentation of AI Use (AI Interaction Log) (15 points)
Evaluates: Transparency and completeness of AI documentation.
| Level | Description | Points |
|---|---|---|
| Excellent | AI interaction log is complete, well-organized, and clearly annotated. Goals, strengths, and limitations of AI responses are explicitly documented. | 14–15 |
| Competent | AI interaction log is mostly complete with basic annotations. Some explanations lack depth or clarity. | 11–13 |
| Developing | AI interaction log is incomplete or weakly annotated. Limited explanation of AI strengths or limitations. | 6–10 |
| Insufficient | AI use is poorly documented, unclear, or missing. | 0–5 |
Critical Evaluation of AI Output (25 points)
Evaluates: Ability to question, critique, and challenge AI responses.
| Level | Description | Points |
|---|---|---|
| Excellent | Clearly identifies specific limitations, assumptions, or errors in AI output and explains why they matter using statistical reasoning. | 23–25 |
| Competent | Identifies some limitations or weaknesses in AI output, but explanations are partial or underdeveloped. | 18–22 |
| Developing | Mentions limitations superficially or inconsistently. Limited justification or reasoning. | 10–17 |
| Insufficient | Accepts AI output uncritically or fails to identify meaningful issues. | 0–9 |
Data Science Understanding and Human Synthesis (30 points)
Evaluates: Depth, accuracy, and originality of the group’s explanation.
| Level | Description | Points |
|---|---|---|
| Excellent | Explanation is accurate, conceptually clear, and fully human-authored. Demonstrates strong statistical reasoning and clearly improves upon AI output. | 27–30 |
| Competent | Explanation is mostly accurate and understandable but adds limited insight beyond AI output. Minor gaps or imprecision. | 21–26 |
| Developing | Explanation shows partial understanding, conceptual gaps, or heavy reliance on AI phrasing. | 12–20 |
| Insufficient | Explanation is incorrect, unclear, or largely AI-generated with minimal human synthesis. | 0–11 |
Communication and Presentation Quality (10 points)
Evaluates: Clarity and structure of the group presentation.
| Level | Description | Points |
|---|---|---|
| Excellent | Presentation is well-structured, clear, and focused on reasoning. Effectively explains what AI missed and what the group learned. | 9–10 |
| Competent | Presentation is understandable but uneven or overly descriptive. Some focus on content rather than reasoning. | 7–8 |
| Developing | Presentation lacks clarity or structure. Key points are unclear or poorly explained. | 4–6 |
| Insufficient | Presentation is disorganized, incomplete, or difficult to follow. | 0–3 |
Individual Adjustment (Reflection-Based)
Individual reflections may be used to adjust individual scores up or down if:
- A student demonstrates substantially stronger or weaker understanding than the group product suggests
- A student did not meaningfully contribute to the group’s work